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Computational Social Science
Rutgers University
Syllabus
Dr. Thomas Davidson
Spring 2022
CONTACT AND LOGISTICS
E-mail: thomas.davidson@rutgers.edu.
Website: https://github.com/t-davidson/css-spring-2022 and Canvas.
Class meetings: MW 2-3:20 p.m, Campbell Hall A5, College Avenue Campus.
Office hours: W 5:00-6:00 p.m, 109 Davison Hall, Douglass Campus or by appointment.
COURSE DESCRIPTION
This course introduces students to the growing field of computational social science. Students will learn
to collect and critically analyze social data using a range of techniques including natural language pro-
cessing, machine learning, and agent-based modeling. We will discuss how these techniques are used by
social scientists and consider the ethical implications of big data and artificial intelligence. Students will
complete homework assignments involving coding in the R programming language to analyze several
different datasets and will complete a group project to create a web-based application for data analysis and
visualization.
LEARNING GOALS
• Become competent at using R and RStudio
• Develop proficiency in data merging, cleaning, and basic analysis
• Understand and implement various methods for online data collection, natural language process, and
machine learning
• Use RShiny to develop a web application for data analysis and visualization
• Identify important ethical issues related to the use of social data and computational methods
PREREQUISITES
Data 101 or equivalent. Enrolled students must have experience writing basic programs in a general purpose
programming language, e.g. R, Python, Java, C. We will review the fundamentals for programming and data
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science in R in weeks 1-3.
ASSESSMENT
• 10% Class participation
– Students are expected to attend all class meetings and to actively participate in class discussions
• 60% Homework assignments (4 x 15%)
– Students will complete a series of homework assignments to gain experience using R for data
science
• 30% Final project
– Students will complete a final project involving the use of RShiny to build an interactive web
application for data analysis and visualization. Students may work individually or as part of a
group.
Rubric
Final grades will be determined according to the following rubric:
• A: 90-100%
• B+: 85-89%
• B: 80-84%
• C+: 75-79%
• C: 70-74%
• D: 60-69%
• F: <60%
READINGS
Most of the readings will consist of chapters from the textbooks listed below. These readings are intended to
build familiarity with key concepts and programming skills. Some weeks there will be an additional reading
to highlight how data science techniques are used in empirical social scientific research. Links to each week’s
readings will be posted on Canvas.
Textbooks
All textbooks are available for free online (hover over titles for links).
• Matthew Salganik. 2017. Bit by Bit. Princeton University Press. ISBN: 0691196109
• Wickham, Hadley, and Garrett Grolemund. 2016. R for Data Science: Import, Tidy, Transform, Visualize,
and Model Data. (R4DS). O’Reilly Media, Inc. ISBN: 1491910399
• Silge, Julia, and David Robinson. 2017. Text Mining with R: A Tidy Approach. O’Reilly Media. ISBN:
1491981652
COURSE RESOURCES
The course will be organized using two different tools, Github and Canvas. Canvas will be used for class
communication, short quizzes, and to host readings. Github Classroom will be used for the submission of
assignments.
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TECHNOLOGY REQUIREMENTS
Students will be required to have access to a computer to complete assignments. Ideally, students should
bring a laptop computer to class.
Please visit the Rutgers Student Tech Guide page for resources available to all students. If you do not have
the appropriate technology for financial reasons, please email Dean of Students deanofstudents@echo.rutger
s.edu for assistance. If you are facing other financial hardships, please visit the Office of Financial Aid.
COURSE POLICIES
The Rutgers Sociology Department strives to create an environment that supports and affirms diversity in
all manifestations, including race, ethnicity, gender, sexual orientation, religion, age, social class, disability
status, region/country of origin, and political orientation. We also celebrate diversity of theoretical and
methodological perspectives among our faculty and students and seek to create an atmosphere of respect
and mutual dialogue. We have zero tolerance for violations of these principles and have instituted clear and
respectful procedures for responding to such grievances.
Students must abide by the Code of Student Conduct at all times, including during lectures and in participa-
tion online.
Students must abide by the university’s Academic Integrity Policy. Violations of academic integrity will
result in disciplinary action. Please review this policy or contact Professor Davidson if there is something
you are unsure about.
If you have a documented disability and require accommodations to obtain equal access in this course,
please contact me during the first week of classes. Students with disabilities must be registered with the Office
of Student Disability Services and must provide verification of their eligibility for such accommodations. See
end of syllabus for further details.
COVID-19: Following university policy, students are required to wear masks at all times during in-person
classes. I recommend students wear an N-95 or equivalent for maximum protection. While the science
is continually evolving, current evidence suggests that cloth masks are ineffective at preventing infection
during periods of sustained social interactions. Please do not attend class or office hours if you have
symptoms or are required to quarantine. If you or your family are affected in any way that impedes your
ability to participate in this class, please contact me as soon as you can so that we can make necessary
arrangements.
COURSE OUTLINE
Week 1, 1/19 (Wednesday only)
Introduction to Computational Social Science
Readings
• Wednesday:
– Bit by Bit, C1
– R4DS: C1 & 27 [Note: Chapter numbers correspond to the online book; physical book numbers
are different]
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Week 2, 1/24 & 1/26
Data Structures in R
Readings
• Monday:
– R4DS: C2,4, skim 20.
• Wednesday:
– R4DS: C17-19, 21.
Week 3, 1/31 & 2/2
Programming Fundamentals
In-Person Instruction Resumes
Readings
• Monday:
– R4DS: C5, 9,
• Wednesday:
– R4DS: C10, 13.
Assignment 1 released: Using R for Data Science.
Week 4, 2/7 & 2/9
Data Collection I: Collecting Data Using Application Programming Interfaces
Readings
• Monday:
– Bit by Bit, C2
• Wednesday:
– R4DS: C3
Week 5, 2/14 & 2/16
Data Collection II: Scraping Data From the Web
Readings
• Monday:
– Bit by Bit, C6
• Wednesday:
– R4DS: C14, 16
Recommended
• Fiesler, Casey, Nate Beard, and Brian C Keegan. 2020. “No Robots, Spiders, or Scrapers: Legal and
Ethical Regulation of Data Collection Methods in Social Media Terms of Service.” In Proceedings of the
Fourteenth International AAAI Conference on Web and Social Media, 187–96.
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Assignment 2: Collecting and storing data released.
Week 6, 2/21 & 2/23
Data Collection III: Online Experiments and Surveys
Readings
• Monday:
– Bit by Bit, C3-5
• Wednesday:
– TBD
– R Shiny tutorial: https://shiny.rstudio.com/tutorial/
Week 7, 2/28 & 3/2
Natural Language Processing I: The Vector-Space Model
Readings
• Monday:
– Evans, James, and Pedro Aceves. 2016. “Machine Translation: Mining Text for Social Theory.”
Annual Review of Sociology 42 (1): 21–50.
• Wednesday:
– Text Mining with R, C1 & 3
Week 8, 3/7 & 3/9
Natural Language Processing II: Word Embeddings
Readings
• Monday:
– Text Mining with R: C5.
• Wednesday:
– Hvitfeldt, Emil and Julia Silge. 2020 Supervised Machine Learning for Text Analysis in R. Chapter 5.
Recommended
• Kozlowski, Austin, Matt Taddy, and James Evans. 2019. “The Geometry of Culture: Analyz-
ing the Meanings of Class through Word Embeddings.” American Sociological Review, September,
000312241987713.
Spring Break
Week 9, 3/21 & 3/23
Natural Language Processing III: Topic Models
Assignment 3: Natural language processing released.
Readings
• Monday:
– Text Mining with R: C6
– Mohr, John, and Petko Bogdanov. 2013. “Introduction—Topic Models: What They Are and Why
They Matter.” Poetics 41 (6): 545–69.
• Wednesday:
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– Roberts, Margaret, Brandon M. Stewart, Dustin Tingley, Christopher Lucas, Jetson Leder-Luis,
Shana Kushner Gadarian, Bethany Albertson, and David Rand. 2014. “Structural Topic Models
for Open-Ended Survey Responses: Structural Topic Models for Survey Responses.” American
Journal of Political Science 58 (4): 1064–82.
Week 10, 3/28 & 3/30
Machine Learning I: Prediction and Explanation
Readings
• Monday:
– Molina, Mario, and Filiz Garip. 2019. “Machine Learning for Sociology.” Annual Review of
Sociology 45: 27–45.
• Wednesday:
– Mullainathan, Sendhil, and Jann Spiess. 2017. “Machine Learning: An Applied Econometric
Approach.” Journal of Economic Perspectives 31 (2): 87–106.
Week 11, 4/4 & 4/6
Machine learning II: Text Classification
Assignment 4: Machine learning released.
Readings
• Monday:
– TBD
• Wednesday:
– Barberá, Pablo, Amber E. Boydstun, Suzanna Linn, Ryan McMahon, and Jonathan Nagler. 2020.
“Automated Text Classification of News Articles: A Practical Guide.” Political Analysis, June, 1–24.
Recommended
• Hanna, Alex. 2013. “Computer-Aided Content Analysis of Digitally Enabled Movements.” Mobilization:
An International Quarterly 18 (4): 367–388.
Week 12, 4/11 & 4/13
Machine learning III: Challenges
Readings
• Monday:
– Salganik, Matthew, Ian Lundberg, Alexander Kindel, et al. 2020. “Measuring the Predictability
of Life Outcomes with a Scientific Mass Collaboration.” Proceedings of the National Academy of
Sciences.
– Buolamwini, Joy, and Timnit Gebru. 2018. “Gender Shades: Intersectional Accuracy Disparities in
Commercial Gender Classification.” In Proceedings of Machine Learning Research, 81:1–15.
• Wednesday:
– Project workshop
Week 13, 4/18 & 4/20
Machine learning IV: Image Classification
Readings
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• Monday:
– Torres, Michelle, and Francisco Cantú. 2021. “Learning to See: Convolutional Neural Networks
for the Analysis of Social Science Data.” Political Analysis, April, 1–19.
– Gebru, Timnit, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Erez Lieberman Aiden, and
Li Fei-Fei. 2017. “Using Deep Learning and Google Street View to Estimate the Demographic
Makeup of Neighborhoods across the United States.” Proceedings of the National Academy of Sciences
114 (50): 13108–13.
• Wednesday:
– Project workshop
Week 14, 4/25 & 4/27
Simulation and Agent-based Models
Readings
• Monday:
– Macy, Michael, and Robert Willer. 2002. “From Factors to Factors: Computational Sociology and
Agent-Based Modeling.” Annual Review of Sociology 28 (1): 143–66.
• Wednesday:
– TBD
Week 15, 5/2 (Monday only)
Project presentations
Final projects due TBD
Additional information
The Rutgers University Student Assembly urges that the following information be included at the end of every syllabus.
Report a Bias Incident
Bias is defined by the University as an act, verbal, written, physical, psychological, that threatens, or harms a
person or group on the basis of race, religion, color, sex, age, sexual orientation, gender identity or expression,
national origin, ancestry, disability, marital status, civil union status, domestic partnership status, atypical
heredity or cellular blood trait, military service or veteran status.
If you experience or witness an act of bias or hate, report it to someone in authority. You may file a report
online and you will be contacted within 24 hours. The bias reporting page is here.
Counseling, ADAP & Psychiatric Services (CAPS)
(848) 932-7884 / 17 Senior Street, New Brunswick, NJ 08901 / Link to website.
CAPS is a University mental health support service that includes counseling, alcohol and other drug
assistance, and psychiatric services staffed by a team of professionals within Rutgers Health services to
support students’ efforts to succeed at Rutgers University. CAPS offers a variety of services that include:
individual therapy, group therapy and workshops, crisis intervention, referral to specialists in the community,
and consultation and collaboration with campus partners.
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Crisis Intervention
Link to website.
Report a Concern:
Link to website.
Violence Prevention & Victim Assistance (VPVA)
(848) 932-1181 / 3 Bartlett Street, New Brunswick, NJ 08901 / Link to website. The Office for Violence
Prevention and Victim Assistance provides confidential crisis intervention, counseling and advocacy
for victims of sexual and relationship violence and stalking to students, staff and faculty. To reach staff
during office hours when the university is open or to reach an advocate after hours, call 848-932-1181.
Disability Services
(848) 445-6800 / Lucy Stone Hall, Suite A145, Livingston Campus, 54 Joyce Kilmer Avenue, Piscataway, NJ
08854 / Link to website
Rutgers University welcomes students with disabilities into all of the University’s educational programs.
In order to receive consideration for reasonable accommodations, a student with a disability must contact
the appropriate disability services office at the campus where you are officially enrolled, participate in an
intake interview, and provide documentation: see guidelines. If the documentation supports your request
for reasonable accommodations, your campus’s disability services office will provide you with a Letter of
Accommodations. Please share this letter with your instructors and discuss the accommodations with them
as early in your courses as possible. To begin this process, please complete the Registration form on the ODS
web site.
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